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CT based intratumor and peritumoral radiomics for differentiating complete from incomplete capsular characteristics of parotid pleomorphic adenoma: a two-center study
OBJECTIVE: Capsular characteristics of pleomorphic adenoma (PA) has various forms. Patients without complete capsule has a higher risk of recurrence than patients with complete capsule. We aimed to develop and validate CT-based intratumoral and peritumoral radiomics models to make a differential dia...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10203084/ https://www.ncbi.nlm.nih.gov/pubmed/37217656 http://dx.doi.org/10.1007/s12672-023-00665-8 |
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author | Li, Shuang Su, Xiaorui Ning, Youquan Zhang, Simin Shao, Hanbing Wan, Xinyue Tan, Qiaoyue Yang, Xibiao Peng, Juan Gong, Qiyong Yue, Qiang |
author_facet | Li, Shuang Su, Xiaorui Ning, Youquan Zhang, Simin Shao, Hanbing Wan, Xinyue Tan, Qiaoyue Yang, Xibiao Peng, Juan Gong, Qiyong Yue, Qiang |
author_sort | Li, Shuang |
collection | PubMed |
description | OBJECTIVE: Capsular characteristics of pleomorphic adenoma (PA) has various forms. Patients without complete capsule has a higher risk of recurrence than patients with complete capsule. We aimed to develop and validate CT-based intratumoral and peritumoral radiomics models to make a differential diagnosis between parotid PA with and without complete capsule. METHODS: Data of 260 patients (166 patients with PA from institution 1 (training set) and 94 patients (test set) from institution 2) were retrospectively analyzed. Three Volume of interest (VOIs) were defined in the CT images of each patient: tumor volume of interest (VOI(tumor)), VOI(peritumor), and VOI(intra-plus peritumor). Radiomics features were extracted from each VOI and used to train nine different machine learning algorithms. Model performance was evaluated using receiver operating characteristic (ROC) curves and the area under the curve (AUC). RESULTS: The results showed that the radiomics models based on features from VOI(intra-plus peritumor) achieved higher AUCs compared to models based on features from VOI(tumor). The best performing model was Linear discriminant analysis, which achieved an AUC of 0.86 in the tenfold cross-validation and 0.869 in the test set. The model was based on 15 features, including shape-based features and texture features. CONCLUSIONS: We demonstrated the feasibility of combining artificial intelligence with CT-based peritumoral radiomics features can be used to accurately predict capsular characteristics of parotid PA. This may assist in clinical decision-making by preoperative identification of capsular characteristics of parotid PA. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12672-023-00665-8. |
format | Online Article Text |
id | pubmed-10203084 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-102030842023-05-24 CT based intratumor and peritumoral radiomics for differentiating complete from incomplete capsular characteristics of parotid pleomorphic adenoma: a two-center study Li, Shuang Su, Xiaorui Ning, Youquan Zhang, Simin Shao, Hanbing Wan, Xinyue Tan, Qiaoyue Yang, Xibiao Peng, Juan Gong, Qiyong Yue, Qiang Discov Oncol Research OBJECTIVE: Capsular characteristics of pleomorphic adenoma (PA) has various forms. Patients without complete capsule has a higher risk of recurrence than patients with complete capsule. We aimed to develop and validate CT-based intratumoral and peritumoral radiomics models to make a differential diagnosis between parotid PA with and without complete capsule. METHODS: Data of 260 patients (166 patients with PA from institution 1 (training set) and 94 patients (test set) from institution 2) were retrospectively analyzed. Three Volume of interest (VOIs) were defined in the CT images of each patient: tumor volume of interest (VOI(tumor)), VOI(peritumor), and VOI(intra-plus peritumor). Radiomics features were extracted from each VOI and used to train nine different machine learning algorithms. Model performance was evaluated using receiver operating characteristic (ROC) curves and the area under the curve (AUC). RESULTS: The results showed that the radiomics models based on features from VOI(intra-plus peritumor) achieved higher AUCs compared to models based on features from VOI(tumor). The best performing model was Linear discriminant analysis, which achieved an AUC of 0.86 in the tenfold cross-validation and 0.869 in the test set. The model was based on 15 features, including shape-based features and texture features. CONCLUSIONS: We demonstrated the feasibility of combining artificial intelligence with CT-based peritumoral radiomics features can be used to accurately predict capsular characteristics of parotid PA. This may assist in clinical decision-making by preoperative identification of capsular characteristics of parotid PA. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12672-023-00665-8. Springer US 2023-05-22 /pmc/articles/PMC10203084/ /pubmed/37217656 http://dx.doi.org/10.1007/s12672-023-00665-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Li, Shuang Su, Xiaorui Ning, Youquan Zhang, Simin Shao, Hanbing Wan, Xinyue Tan, Qiaoyue Yang, Xibiao Peng, Juan Gong, Qiyong Yue, Qiang CT based intratumor and peritumoral radiomics for differentiating complete from incomplete capsular characteristics of parotid pleomorphic adenoma: a two-center study |
title | CT based intratumor and peritumoral radiomics for differentiating complete from incomplete capsular characteristics of parotid pleomorphic adenoma: a two-center study |
title_full | CT based intratumor and peritumoral radiomics for differentiating complete from incomplete capsular characteristics of parotid pleomorphic adenoma: a two-center study |
title_fullStr | CT based intratumor and peritumoral radiomics for differentiating complete from incomplete capsular characteristics of parotid pleomorphic adenoma: a two-center study |
title_full_unstemmed | CT based intratumor and peritumoral radiomics for differentiating complete from incomplete capsular characteristics of parotid pleomorphic adenoma: a two-center study |
title_short | CT based intratumor and peritumoral radiomics for differentiating complete from incomplete capsular characteristics of parotid pleomorphic adenoma: a two-center study |
title_sort | ct based intratumor and peritumoral radiomics for differentiating complete from incomplete capsular characteristics of parotid pleomorphic adenoma: a two-center study |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10203084/ https://www.ncbi.nlm.nih.gov/pubmed/37217656 http://dx.doi.org/10.1007/s12672-023-00665-8 |
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